Year
2013
Season
Summer
Paper Type
Master's Thesis
College
College of Computing, Engineering & Construction
Degree Name
Master of Science in Computer and Information Sciences (MS)
Department
Computing
NACO controlled Corporate Body
University of North Florida. School of Computing
First Advisor
Dr. Sanjay P. Ahuja
Second Advisor
Dr. Roger Eggen
Third Advisor
Dr. Zornitza G. Prodanoff
Department Chair
Dr. Asai Asaithambi
College Dean
Dr. Mark A. Tumeo
Abstract
Big data is a topic of active research in the cloud community. With increasing demand for data storage in the cloud, study of data-intensive applications is becoming a primary focus. Data-intensive applications involve high CPU usage for processing large volumes of data on the scale of terabytes or petabytes. While some research exists for the performance effect of data intensive applications in the cloud, none of the research compares the Amazon Elastic Compute Cloud (Amazon EC2) and Google Compute Engine (GCE) clouds using multiple benchmarks. This study performs extensive research on the Amazon EC2 and GCE clouds using the TeraSort, MalStone and CreditStone benchmarks on Hadoop and Sector data layers. Data collected for the Amazon EC2 and GCE clouds measure performance as the number of nodes is varied. This study shows that GCE is more efficient for data-intensive applications compared to Amazon EC2.
Suggested Citation
Kaza, Bhagavathi, "Performance Evaluation of Data Intensive Computing In The Cloud" (2013). UNF Graduate Theses and Dissertations. 450.
https://digitalcommons.unf.edu/etd/450